Many individuals on SGLT2 inhibitors are on concomitant statin medications. The risk of concurrent medication use and risk of myotoxicity is unknown.
We aim to evaluate the association of concurrent SGLT2 inhibitor and statin medication use and risk of myotoxicity and elevated creatinine kinase levels.
For this analysis, we will use the CANVAS trial dataset to evaluate creatinine kinase levels among individuals on both SGLT2 inhibitors and statin medications, on one of the two, and on neither.
All patients. We will exclude participants with missing medication data.
Main Outcome Measures:
Our main outcome of interest is creatinine kinase level over time.
Linear mixed effect models will be devised to evaluate creatine kinase levels over time. We will calculate the least squared means to evaluate creatinine kinase levels over time between different medication groups.
The sodium-glucose cotransporter 2 inhibitors (SGLT2i), a class of glucose-lowering therapies, have been shown to reduce risk of HF in at-risk patients with T2DM (type 2 diabetes mellitus), and are now supported as second-line therapies (after metformin) in patients with T2DM and cardiovascular risk factors or with prevalent ASCVD (atherosclerotic cardiovascular disease)[1-3]. However, limited guidance is available regarding concurrent SGLT2i use and statin medications. Since many individuals at risk for cardiovascular disease are also on statin medications, it is unclear if there is an increased risk of myotoxicity with concurrent use.
As such, we aimed to evaluate the association of concurrent SGLT2i and statin use and risk of elevated creatinine kinase levels among individuals from the CANVAS trial.
For this project, we aim to evaluate the association of concurrent SGLT2i and statin use and risk of elevated creatinine kinase levels among individuals from the CANVAS trial.
We will exclude participants with missing concurrent medication data
Our primary outcome of interest is creatinine kinase level over time. Our secondary outcome of interest is AST level over time.
Our main predictor variable is a 4 category variable: 1) concurrent SGLT2i and statin use, 2) only SGLT2i use, 3) only statin use, 4) neither SGLT2i or statin use.
We will further adjust models for age, sex, race, creatinine level, body mass index, and trial site.
Individuals will be categorized into one of 4 categories: 1) concurrent SGLT2i and statin use, 2) only SGLT2i use, 3) only statin use, 4) neither SGLT2i or statin use. We will then evaluate the trend in creatinine kinase levels over time using linear mixed effect models. The least square means will be calculated for each of the 4 groups and differences between groups will be determined. Differences will be reported as estimate and 95% confidence interval. Analyses all be performed using R with a P < 0.05 indicating significance.
Many patients on SGLT2 inhibitors for diabetes control are also on statin medications to lower cholesterol. SGLT2 medications have lower drug-drug interactions compared to other classes of diabetes medications. However, the association of SGLT2 medication and concurrent statin medication use is unknown. We intend to evaluate the levels of creatinine kinase, a protein found in muscle, among individuals on SGLT2 and statin medications. We will compare the protein levels to individuals on SGLT2 or statin medications alone and on neither.
We hypothesize that concurrent SGLT2 and statin medication use will not raise creatinine kinase levels.
Since we have a prior DUA for CANVAS data, we hope for an expedited timeline.
1 month: project evaluation/review
1 month: reform analysis
1 month: write manuscript
1 month: submit manuscript proposal to YODA
3 months: submit manuscript and revise based on reviewer comments
1 month: report results back to YODA
We plan to publish this project in a general medicine journal such as Annals of Internal Medicine or American Journal of Medicine.
Degorter, Marianne K., et al. “Clinical and Pharmacogenetic Predictors of Circulating Atorvastatin and Rosuvastatin Concentrations in Routine Clinical Care.” Circulation: Cardiovascular Genetics, vol. 6, no. 4, 2013, pp. 400–408., doi:10.1161/circgenetics.113.000099.
Mamidi, Rao N. V. S., et al. “In Vitro and Physiologically‐Based Pharmacokinetic Based Assessment of Drug–Drug Interaction Potential of Canagliflozin.” British Journal of Clinical Pharmacology, vol. 83, no. 5, 2016, pp. 1082–1096., doi:10.1111/bcp.13186.
Perkovic, Vlado, et al. “Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy.” New England Journal of Medicine, vol. 380, no. 24, 2019, pp. 2295–2306., doi:10.1056/nejmoa1811744.
Brailovski, Eugene, et al. “Rosuvastatin Myotoxicity After Starting Canagliflozin Treatment: A Case Report.” Annals of Internal Medicine, 2020, doi:10.7326/l20-0549.